大模型有一些结构性弱点，直接限制了智能体在真实业务中的应用价值，因此智能体工程的一大核心工作，就是在模型外围，用工程手段补齐短板、设置边界、约束行为。
Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04
在过年给小孩挑选礼物时，我就陷入了一个巨大的AI玩具坑。从挂件、机器狗到毛绒玩具，从早教机器人、养成系电子宠物到智能成长搭子，凡是挂上AI的名号，就好像自动拥有了陪伴孩子一起成长的魔力。，推荐阅读Line官方版本下载获取更多信息
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Go to worldnews，这一点在heLLoword翻译官方下载中也有详细论述
In the months since, I continued my real-life work as a Data Scientist while keeping up-to-date on the latest LLMs popping up on OpenRouter. In August, Google announced the release of their Nano Banana generative image AI with a corresponding API that’s difficult to use, so I open-sourced the gemimg Python package that serves as an API wrapper. It’s not a thrilling project: there’s little room or need for creative implementation and my satisfaction with it was the net present value with what it enabled rather than writing the tool itself. Therefore as an experiment, I plopped the feature-complete code into various up-and-coming LLMs on OpenRouter and prompted the models to identify and fix any issues with the Python code: if it failed, it’s a good test for the current capabilities of LLMs, if it succeeded, then it’s a software quality increase for potential users of the package and I have no moral objection to it. The LLMs actually were helpful: in addition to adding good function docstrings and type hints, it identified more Pythonic implementations of various code blocks.